SimDesign: ECR – R documentation

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ECR

Compute empirical coverage rates

Description

Computes the detection rate for determining empirical coverage rates given a set of estimated confidence intervals. Note that using 1 - ECR(CIs, parameter) will provide the empirical detection rate. Also supports computing the average width of the CIs, which may be useful when comparing the efficiency of CI estimators.

Usage

ECR(
  CIs,
  parameter,
  tails = FALSE,
  CI_width = FALSE,
  names = NULL,
  unname = FALSE
)

Arguments

`CIs`	a `numeric` vector or `matrix` of confidence interval values for a given parameter value, where the first element/column indicates the lower confidence interval and the second element/column the upper confidence interval. If a vector of length 2 is passed instead then the returned value will be either a 1 or 0 to indicate whether the parameter value was or was not within the interval, respectively. Otherwise, the input must be a matrix with an even number of columns
`parameter`	a numeric scalar indicating the fixed parameter value. Alternative, a `numeric` vector object with length equal to the number of rows as `CIs` (use to compare sets of parameters at once)
`tails`	logical; when TRUE returns a vector of length 2 to indicate the proportion of times the parameter was lower or higher than the supplied interval, respectively. This is mainly only useful when the coverage region is not expected to be symmetric, and therefore is generally not required. Note that `1 - sum(ECR(CIs, parameter, tails=TRUE)) == ECR(CIs, parameter)`
`CI_width`	logical; rather than returning the overall coverage rate, return the average width of the CIs instead? Useful when comparing the efficiency of different CI estimators
`names`	an optional character vector used to name the returned object. Generally useful when more than one CI estimate is investigated at once
`unname`	logical; apply `unname` to the results to remove any variable names?

Author(s)

Phil Chalmers rphilip.chalmers@gmail.com

References

Chalmers, R. P., & Adkins, M. C. (2020). Writing Effective and Reliable Monte Carlo Simulations with the SimDesign Package. The Quantitative Methods for Psychology, 16(4), 248-280. doi: 10.20982/tqmp.16.4.p248

Sigal, M. J., & Chalmers, R. P. (2016). Play it again: Teaching statistics with Monte Carlo simulation. Journal of Statistics Education, 24(3), 136-156. doi: 10.1080/10691898.2016.1246953

Examples

CIs <- matrix(NA, 100, 2)
for(i in 1:100){
   dat <- rnorm(100)
   CIs[i,] <- t.test(dat)$conf.int
}

ECR(CIs, 0)
ECR(CIs, 0, tails = TRUE)

# single vector input
CI <- c(-1, 1)
ECR(CI, 0)
ECR(CI, 2)
ECR(CI, 2, tails = TRUE)

# parameters of the same size as CI
parameters <- 1:10
CIs <- cbind(parameters - runif(10), parameters + runif(10))
parameters <- parameters + rnorm(10)
ECR(CIs, parameters)

# average width of CIs
ECR(CIs, parameters, CI_width=TRUE)

# ECR() for multiple CI estimates in the same object
parameter <- 10
CIs <- data.frame(lowerCI_1=parameter - runif(10),
                  upperCI_1=parameter + runif(10),
                  lowerCI_2=parameter - 2*runif(10),
                  upperCI_2=parameter + 2*runif(10))
head(CIs)
ECR(CIs, parameter)
ECR(CIs, parameter, tails=TRUE)
ECR(CIs, parameter, CI_width=TRUE)

# often a good idea to provide names for the output
ECR(CIs, parameter, names = c('this', 'that'))
ECR(CIs, parameter, CI_width=TRUE, names = c('this', 'that'))
ECR(CIs, parameter, tails=TRUE, names = c('this', 'that'))

SimDesign

Structure for Organizing Monte Carlo Simulation Designs

v2.3

GPL (>= 2)

Authors

Phil Chalmers [aut, cre] (<https://orcid.org/0000-0001-5332-2810>), Matthew Sigal [ctb], Ogreden Oguzhan [ctb]

Initial release

ECR